Sébastien Rouault — accepted AI-conference papers

2021

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

El Mahdi El Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê-Nguyên Hoang, Sébastien Rouault

NeurIPS 2021poster

We study \emph{Byzantine collaborative learning}, where $n$ nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node is trusted, and $f < n$ nodes can behave arbitrarily. We prove that collaborative learning is equivalent to a ne…

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2021

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault

ICLR 2021poster

Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting. Two recent attacks have been challenging state-of-th…

Cited by 67 Source ↗PDF ↗Scholar ↗

2018

The Hidden Vulnerability of Distributed Learning in Byzantium

El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault

ICML 2018oral

While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending

Cited by 788 Source ↗PDF ↗code ↗Scholar ↗